old_README.md

Analysis of 125+ Linux configurations (this time for predicting/understanding kernel sizes)

• git clone https://github.com/TuxML/size-analysis/ (to get tuxml.py)
• git clone https://gitlab.com/FAMILIAR-project/tuxml-size-analysis-datasets/ (to get datasets)
• then you can use tuxml.py to load a pre-encoded dataset (it returns a pandas dataframe):

import tuxml
df = tuxml.load_dataset()

An example is given with size-analysis-fast.ipynb Note: the datatset is loaded here: ../tuxml-size-analysis-datasets/all_size_withyes.pkl so be careful about relative paths and your git repo locations

HOW TO analyse_kconfig_help_msg.py

First install Kconfiglib pip[3] install kconfiglib

To realize Patch Kernel Makefile: git clone https://github.com/ulfalizer/Kconfiglib.git Download a Linux kernel ie in our case: https://cdn.kernel.org/pub/linux/kernel/v4.x/linux-4.13.3.tar.xz In the kernel top-directory: cd linux-4.13.3 and then patch -p1 < ../Kconfiglib/makefile.patch (it will modify the Makefile of linux kernel to support some commands like scriptconfig see below)

Finally, you can use the script: always in the kernel directory linux-4.13.3, you can run: make ARCH=x86 scriptconfig SCRIPT=../analyse_kconfig_help_msg.py

Docker image

docker build -f docker/Dockerfile -t sklearntux . (it can take a while) or simply docker pull macher/sklearntux

docker run -it --rm macher/sklearntux python3 size-analysis-fast.py should work

Notes:

• there is a all_size_withyes.pkl pre-copied (it is a .pkl of the dataset) -- it can a CSV file as well
• plotting facilities are installed (matplotlib, seaborn, etc.) partly explaining the increase in size of the Docker image